Radiogenomics for Precision Medicine With a Big Data Analytics Perspective
Open accessAbstract
Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.
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Joint radiogenomic signatures were found to increase the predictive ability of generated models in glioma phenotype group.
“At the same time, hypothesis testing in glioma phenotype group verified the significant association between certain radiomic and genomic features with respect to overall and progression free survival, while joint radiogenomic signatures were found to increase the predictive ability of generated models.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [68]
Glioma phenotype group hypothesis testing verified a significant association between certain radiomic and genomic features with respect to overall and progression free survival.
“At the same time, hypothesis testing in glioma phenotype group verified the significant association between certain radiomic and genomic features with respect to overall and progression free survival, while joint radiogenomic signatures were found to increase the predictive ability of generated models.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [68]
Imaging features were linked to molecular GBM subtype classification (based on Verhaak and/or Philips classification) providing for non-invasive prognosis.
“Importantly, imaging features were linked to molecular GBM subtype classification (based on Verhaak and/ or Philips classification) providing for non-invasive prognosis [68], [240].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [68]
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- Authors
- Panayides, A. S., Pattichis, M. S., Leandrou, S., Pitris, C., Constantinidou, A., & Pattichis, C. S.
- Affiliations
- University of Cyprus (CY)
- University of New Mexico (US)
- European University Cyprus (CY)
- ORCID
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2019-09
- Volume
- 23
- Issue
- 5
- Pages
- 2063-2079
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2905881943
- Cited by
- 66
- References
- 21
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 11 · 2023: 13 · 2022: 7 · 2021: 11 · 2020: 10 · 2019: 2 · 2018: 1
- FWCI
- 4.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Integrated Precision Medicine Technologies (763781)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- radiogenomics, radiomics, precision medicine, big data analytics, cancer, Alzheimer's disease, data standardization, open data initiatives, computational challenges, knowledge discovery
- MeSH terms
- Deep Learning, Aged, Algorithms, Alzheimer Disease, Female, Humans, Male, Middle Aged, Neoplasms, Radiographic Image Interpretation, Computer-Assisted, Radiography, Genomics, Genome-Wide Association Study, Precision Medicine
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